---
title: Rebuild graph at runtime
sidebarTitle: Rebuild graph at runtime
---
You might need to rebuild your graph with a different configuration for a new run. For example, you might need to use a different graph state or graph structure depending on the config. This guide shows how you can do this.

<Note>
**Note**
In most cases, customizing behavior based on the config should be handled by a single graph where each node can read a config and change its behavior based on it
</Note>

## Prerequisites

Make sure to check out [this how-to guide](/langgraph-platform/setup-app-requirements-txt) on setting up your app for deployment first.

## Define graphs

Let's say you have an app with a simple graph that calls an LLM and returns the response to the user. The app file directory looks like the following:

```
my-app/
|-- requirements.txt
|-- .env
|-- openai_agent.py     # code for your graph
```

where the graph is defined in `openai_agent.py`.

### No rebuild

In the standard LangGraph API configuration, the server uses the compiled graph instance that's defined at the top level of `openai_agent.py`, which looks like the following:

```python
from langchain_openai import ChatOpenAI
from langgraph.graph import END, START, MessageGraph

model = ChatOpenAI(temperature=0)

graph_workflow = MessageGraph()

graph_workflow.add_node("agent", model)
graph_workflow.add_edge("agent", END)
graph_workflow.add_edge(START, "agent")

agent = graph_workflow.compile()
```

To make the server aware of your graph, you need to specify a path to the variable that contains the `CompiledStateGraph` instance in your LangGraph API configuration (`langgraph.json`), e.g.:

```
{
    "dependencies": ["."],
    "graphs": {
        "openai_agent": "./openai_agent.py:agent",
    },
    "env": "./.env"
}
```

### Rebuild

To make your graph rebuild on each new run with custom configuration, you need to rewrite `openai_agent.py` to instead provide a _function_ that takes a config and returns a graph (or compiled graph) instance. Let's say we want to return our existing graph for user ID '1', and a tool-calling agent for other users. We can modify `openai_agent.py` as follows:

```python
from typing import Annotated
from typing_extensions import TypedDict
from langchain_openai import ChatOpenAI
from langgraph.graph import END, START, MessageGraph
from langgraph.graph.state import StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode
from langchain_core.tools import tool
from langchain_core.messages import BaseMessage
from langchain_core.runnables import RunnableConfig


class State(TypedDict):
    messages: Annotated[list[BaseMessage], add_messages]


model = ChatOpenAI(temperature=0)

def make_default_graph():
    """Make a simple LLM agent"""
    graph_workflow = StateGraph(State)
    def call_model(state):
        return {"messages": [model.invoke(state["messages"])]}

    graph_workflow.add_node("agent", call_model)
    graph_workflow.add_edge("agent", END)
    graph_workflow.add_edge(START, "agent")

    agent = graph_workflow.compile()
    return agent


def make_alternative_graph():
    """Make a tool-calling agent"""

    @tool
    def add(a: float, b: float):
        """Adds two numbers."""
        return a + b

    tool_node = ToolNode([add])
    model_with_tools = model.bind_tools([add])
    def call_model(state):
        return {"messages": [model_with_tools.invoke(state["messages"])]}

    def should_continue(state: State):
        if state["messages"][-1].tool_calls:
            return "tools"
        else:
            return END

    graph_workflow = StateGraph(State)

    graph_workflow.add_node("agent", call_model)
    graph_workflow.add_node("tools", tool_node)
    graph_workflow.add_edge("tools", "agent")
    graph_workflow.add_edge(START, "agent")
    graph_workflow.add_conditional_edges("agent", should_continue)

    agent = graph_workflow.compile()
    return agent


# this is the graph making function that will decide which graph to
# build based on the provided config
def make_graph(config: RunnableConfig):
    user_id = config.get("configurable", {}).get("user_id")
    # route to different graph state / structure based on the user ID
    if user_id == "1":
        return make_default_graph()
    else:
        return make_alternative_graph()
```

Finally, you need to specify the path to your graph-making function (`make_graph`) in `langgraph.json`:

```
{
    "dependencies": ["."],
    "graphs": {
        "openai_agent": "./openai_agent.py:make_graph",
    },
    "env": "./.env"
}
```

See more info on LangGraph API configuration file [here](/langgraph-platform/cli#configuration-file)
